Overview

Dataset statistics

Number of variables63
Number of observations266
Missing cells15288
Missing cells (%)91.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory160.5 KiB
Average record size in memory617.9 B

Variable types

Categorical12
Unsupported26
Numeric25

Alerts

1985 has constant value "17.7688980102539" Constant
1986 has constant value "16.7716026306152" Constant
1987 has constant value "11.2920875549316" Constant
1988 has constant value "12.3102493286133" Constant
Country Name has a high cardinality: 266 distinct values High cardinality
Country Code has a high cardinality: 266 distinct values High cardinality
1992 is highly correlated with 1997 and 7 other fieldsHigh correlation
1993 is highly correlated with 1998 and 12 other fieldsHigh correlation
1994 is highly correlated with 2006 and 7 other fieldsHigh correlation
1995 is highly correlated with 2000 and 12 other fieldsHigh correlation
1996 is highly correlated with 1997 and 14 other fieldsHigh correlation
1997 is highly correlated with 1992 and 17 other fieldsHigh correlation
1998 is highly correlated with 1992 and 23 other fieldsHigh correlation
1999 is highly correlated with 1993 and 22 other fieldsHigh correlation
2000 is highly correlated with 1993 and 23 other fieldsHigh correlation
2001 is highly correlated with 1993 and 24 other fieldsHigh correlation
2002 is highly correlated with 1992 and 22 other fieldsHigh correlation
2003 is highly correlated with 1993 and 23 other fieldsHigh correlation
2004 is highly correlated with 1992 and 24 other fieldsHigh correlation
2005 is highly correlated with 1993 and 22 other fieldsHigh correlation
2006 is highly correlated with 1993 and 23 other fieldsHigh correlation
2007 is highly correlated with 1993 and 22 other fieldsHigh correlation
2008 is highly correlated with 1992 and 22 other fieldsHigh correlation
2009 is highly correlated with 1993 and 24 other fieldsHigh correlation
2010 is highly correlated with 1993 and 23 other fieldsHigh correlation
2011 is highly correlated with 1994 and 24 other fieldsHigh correlation
2012 is highly correlated with 1992 and 23 other fieldsHigh correlation
2013 is highly correlated with 1995 and 23 other fieldsHigh correlation
2014 is highly correlated with 1992 and 27 other fieldsHigh correlation
2015 is highly correlated with 1994 and 22 other fieldsHigh correlation
2016 is highly correlated with 1994 and 23 other fieldsHigh correlation
2017 is highly correlated with 1994 and 23 other fieldsHigh correlation
2018 is highly correlated with 1992 and 23 other fieldsHigh correlation
2019 is highly correlated with 1997 and 15 other fieldsHigh correlation
2020 is highly correlated with 1997 and 16 other fieldsHigh correlation
1992 is highly correlated with 1997 and 6 other fieldsHigh correlation
1993 is highly correlated with 1998 and 10 other fieldsHigh correlation
1994 is highly correlated with 2006 and 7 other fieldsHigh correlation
1995 is highly correlated with 2000 and 12 other fieldsHigh correlation
1996 is highly correlated with 1997 and 15 other fieldsHigh correlation
1997 is highly correlated with 1992 and 18 other fieldsHigh correlation
1998 is highly correlated with 1992 and 16 other fieldsHigh correlation
1999 is highly correlated with 1993 and 20 other fieldsHigh correlation
2000 is highly correlated with 1993 and 22 other fieldsHigh correlation
2001 is highly correlated with 1995 and 22 other fieldsHigh correlation
2002 is highly correlated with 1993 and 21 other fieldsHigh correlation
2003 is highly correlated with 1993 and 23 other fieldsHigh correlation
2004 is highly correlated with 1992 and 23 other fieldsHigh correlation
2005 is highly correlated with 1993 and 23 other fieldsHigh correlation
2006 is highly correlated with 1993 and 21 other fieldsHigh correlation
2007 is highly correlated with 1993 and 21 other fieldsHigh correlation
2008 is highly correlated with 1992 and 21 other fieldsHigh correlation
2009 is highly correlated with 1994 and 23 other fieldsHigh correlation
2010 is highly correlated with 1993 and 21 other fieldsHigh correlation
2011 is highly correlated with 1994 and 24 other fieldsHigh correlation
2012 is highly correlated with 1992 and 21 other fieldsHigh correlation
2013 is highly correlated with 1995 and 22 other fieldsHigh correlation
2014 is highly correlated with 1992 and 24 other fieldsHigh correlation
2015 is highly correlated with 1994 and 23 other fieldsHigh correlation
2016 is highly correlated with 1994 and 22 other fieldsHigh correlation
2017 is highly correlated with 1994 and 22 other fieldsHigh correlation
2018 is highly correlated with 1992 and 21 other fieldsHigh correlation
2019 is highly correlated with 1997 and 16 other fieldsHigh correlation
2020 is highly correlated with 1997 and 11 other fieldsHigh correlation
1992 is highly correlated with 1997 and 6 other fieldsHigh correlation
1993 is highly correlated with 1999 and 9 other fieldsHigh correlation
1994 is highly correlated with 2006 and 7 other fieldsHigh correlation
1995 is highly correlated with 2000 and 7 other fieldsHigh correlation
1996 is highly correlated with 1997 and 6 other fieldsHigh correlation
1997 is highly correlated with 1992 and 11 other fieldsHigh correlation
1998 is highly correlated with 1992 and 14 other fieldsHigh correlation
1999 is highly correlated with 1993 and 19 other fieldsHigh correlation
2000 is highly correlated with 1993 and 16 other fieldsHigh correlation
2001 is highly correlated with 1996 and 20 other fieldsHigh correlation
2002 is highly correlated with 1993 and 19 other fieldsHigh correlation
2003 is highly correlated with 1993 and 17 other fieldsHigh correlation
2004 is highly correlated with 1992 and 21 other fieldsHigh correlation
2005 is highly correlated with 1993 and 21 other fieldsHigh correlation
2006 is highly correlated with 1993 and 21 other fieldsHigh correlation
2007 is highly correlated with 1993 and 18 other fieldsHigh correlation
2008 is highly correlated with 1992 and 21 other fieldsHigh correlation
2009 is highly correlated with 1994 and 21 other fieldsHigh correlation
2010 is highly correlated with 1993 and 21 other fieldsHigh correlation
2011 is highly correlated with 1994 and 23 other fieldsHigh correlation
2012 is highly correlated with 1992 and 19 other fieldsHigh correlation
2013 is highly correlated with 1995 and 20 other fieldsHigh correlation
2014 is highly correlated with 1992 and 23 other fieldsHigh correlation
2015 is highly correlated with 1994 and 19 other fieldsHigh correlation
2016 is highly correlated with 1994 and 19 other fieldsHigh correlation
2017 is highly correlated with 1994 and 19 other fieldsHigh correlation
2018 is highly correlated with 1992 and 20 other fieldsHigh correlation
2019 is highly correlated with 1997 and 13 other fieldsHigh correlation
2020 is highly correlated with 1997 and 11 other fieldsHigh correlation
1992 is highly correlated with 2002 and 1 other fieldsHigh correlation
1993 is highly correlated with 1998 and 2 other fieldsHigh correlation
1994 is highly correlated with 2011High correlation
1995 is highly correlated with 2000 and 9 other fieldsHigh correlation
1996 is highly correlated with 1999 and 11 other fieldsHigh correlation
1997 is highly correlated with 1998 and 15 other fieldsHigh correlation
1998 is highly correlated with 1993 and 15 other fieldsHigh correlation
1999 is highly correlated with 1996 and 14 other fieldsHigh correlation
2000 is highly correlated with 1995 and 16 other fieldsHigh correlation
2001 is highly correlated with 1993 and 19 other fieldsHigh correlation
2002 is highly correlated with 1992 and 18 other fieldsHigh correlation
2003 is highly correlated with 1995 and 14 other fieldsHigh correlation
2004 is highly correlated with 1992 and 23 other fieldsHigh correlation
2005 is highly correlated with 1995 and 19 other fieldsHigh correlation
2006 is highly correlated with 1995 and 16 other fieldsHigh correlation
2007 is highly correlated with 1995 and 22 other fieldsHigh correlation
2008 is highly correlated with 1996 and 18 other fieldsHigh correlation
2009 is highly correlated with 1993 and 19 other fieldsHigh correlation
2010 is highly correlated with 1995 and 22 other fieldsHigh correlation
2011 is highly correlated with 1994 and 20 other fieldsHigh correlation
2012 is highly correlated with 1998 and 18 other fieldsHigh correlation
2013 is highly correlated with 1995 and 20 other fieldsHigh correlation
2014 is highly correlated with 1996 and 20 other fieldsHigh correlation
2015 is highly correlated with 1995 and 18 other fieldsHigh correlation
2016 is highly correlated with 1995 and 17 other fieldsHigh correlation
2017 is highly correlated with 1995 and 18 other fieldsHigh correlation
2018 is highly correlated with 1996 and 13 other fieldsHigh correlation
2019 is highly correlated with 1998 and 16 other fieldsHigh correlation
2020 is highly correlated with 2002 and 4 other fieldsHigh correlation
1960 has 266 (100.0%) missing values Missing
1961 has 266 (100.0%) missing values Missing
1962 has 266 (100.0%) missing values Missing
1963 has 266 (100.0%) missing values Missing
1964 has 266 (100.0%) missing values Missing
1965 has 266 (100.0%) missing values Missing
1966 has 266 (100.0%) missing values Missing
1967 has 266 (100.0%) missing values Missing
1968 has 266 (100.0%) missing values Missing
1969 has 266 (100.0%) missing values Missing
1970 has 266 (100.0%) missing values Missing
1971 has 266 (100.0%) missing values Missing
1972 has 266 (100.0%) missing values Missing
1973 has 266 (100.0%) missing values Missing
1974 has 266 (100.0%) missing values Missing
1975 has 266 (100.0%) missing values Missing
1976 has 266 (100.0%) missing values Missing
1977 has 266 (100.0%) missing values Missing
1978 has 266 (100.0%) missing values Missing
1979 has 266 (100.0%) missing values Missing
1980 has 266 (100.0%) missing values Missing
1981 has 266 (100.0%) missing values Missing
1982 has 266 (100.0%) missing values Missing
1983 has 266 (100.0%) missing values Missing
1984 has 266 (100.0%) missing values Missing
1985 has 265 (99.6%) missing values Missing
1986 has 265 (99.6%) missing values Missing
1987 has 265 (99.6%) missing values Missing
1988 has 265 (99.6%) missing values Missing
1989 has 266 (100.0%) missing values Missing
1990 has 263 (98.9%) missing values Missing
1991 has 264 (99.2%) missing values Missing
1992 has 262 (98.5%) missing values Missing
1993 has 262 (98.5%) missing values Missing
1994 has 263 (98.9%) missing values Missing
1995 has 256 (96.2%) missing values Missing
1996 has 250 (94.0%) missing values Missing
1997 has 251 (94.4%) missing values Missing
1998 has 245 (92.1%) missing values Missing
1999 has 243 (91.4%) missing values Missing
2000 has 223 (83.8%) missing values Missing
2001 has 238 (89.5%) missing values Missing
2002 has 230 (86.5%) missing values Missing
2003 has 229 (86.1%) missing values Missing
2004 has 226 (85.0%) missing values Missing
2005 has 209 (78.6%) missing values Missing
2006 has 225 (84.6%) missing values Missing
2007 has 223 (83.8%) missing values Missing
2008 has 228 (85.7%) missing values Missing
2009 has 220 (82.7%) missing values Missing
2010 has 203 (76.3%) missing values Missing
2011 has 227 (85.3%) missing values Missing
2012 has 222 (83.5%) missing values Missing
2013 has 226 (85.0%) missing values Missing
2014 has 222 (83.5%) missing values Missing
2015 has 208 (78.2%) missing values Missing
2016 has 215 (80.8%) missing values Missing
2017 has 224 (84.2%) missing values Missing
2018 has 240 (90.2%) missing values Missing
2019 has 252 (94.7%) missing values Missing
2020 has 263 (98.9%) missing values Missing
Country Name is uniformly distributed Uniform
Country Code is uniformly distributed Uniform
1990 is uniformly distributed Uniform
1991 is uniformly distributed Uniform
1992 is uniformly distributed Uniform
1993 is uniformly distributed Uniform
1994 is uniformly distributed Uniform
2020 is uniformly distributed Uniform
Country Name has unique values Unique
Country Code has unique values Unique
1960 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1961 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1962 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1963 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1964 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1965 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1966 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1967 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1968 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1969 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1970 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1971 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1972 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1973 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1974 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1975 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1976 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1977 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1978 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1979 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1980 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1981 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1982 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1983 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1984 is an unsupported type, check if it needs cleaning or further analysis Unsupported
1989 is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2022-04-03 16:38:19.093198
Analysis finished2022-04-03 16:39:23.086067
Duration1 minute and 3.99 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Country Name
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct266
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size18.2 KiB
Aruba
 
1
Oman
 
1
Malawi
 
1
Malaysia
 
1
North America
 
1
Other values (261)
261 

Length

Max length52
Median length9
Mean length12.40225564
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique266 ?
Unique (%)100.0%

Sample

1st rowAruba
2nd rowAfrica Eastern and Southern
3rd rowAfghanistan
4th rowAfrica Western and Central
5th rowAngola

Common Values

ValueCountFrequency (%)
Aruba1
 
0.4%
Oman1
 
0.4%
Malawi1
 
0.4%
Malaysia1
 
0.4%
North America1
 
0.4%
Namibia1
 
0.4%
New Caledonia1
 
0.4%
Niger1
 
0.4%
Nigeria1
 
0.4%
Nicaragua1
 
0.4%
Other values (256)256
96.2%

Length

2022-04-03T11:39:23.171899image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20
 
4.0%
and12
 
2.4%
income11
 
2.2%
ida10
 
2.0%
islands9
 
1.8%
africa9
 
1.8%
ibrd8
 
1.6%
asia8
 
1.6%
countries7
 
1.4%
rep7
 
1.4%
Other values (310)404
80.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Country Code
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct266
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size15.7 KiB
ABW
 
1
OMN
 
1
MWI
 
1
MYS
 
1
NAC
 
1
Other values (261)
261 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique266 ?
Unique (%)100.0%

Sample

1st rowABW
2nd rowAFE
3rd rowAFG
4th rowAFW
5th rowAGO

Common Values

ValueCountFrequency (%)
ABW1
 
0.4%
OMN1
 
0.4%
MWI1
 
0.4%
MYS1
 
0.4%
NAC1
 
0.4%
NAM1
 
0.4%
NCL1
 
0.4%
NER1
 
0.4%
NGA1
 
0.4%
NIC1
 
0.4%
Other values (256)256
96.2%

Length

2022-04-03T11:39:23.276178image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
abw1
 
0.4%
aut1
 
0.4%
btn1
 
0.4%
brn1
 
0.4%
afg1
 
0.4%
afw1
 
0.4%
ago1
 
0.4%
alb1
 
0.4%
and1
 
0.4%
arb1
 
0.4%
Other values (256)256
96.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

1960
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1961
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1962
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1963
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1964
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1965
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1966
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1967
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1968
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1969
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1970
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1971
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1972
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1973
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1974
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1975
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1976
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1977
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1978
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1979
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1980
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1981
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1982
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1983
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1984
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1985
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)100.0%
Missing265
Missing (%)99.6%
Memory size10.5 KiB
17.7688980102539

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row17.7688980102539

Common Values

ValueCountFrequency (%)
17.76889801025391
 
0.4%
(Missing)265
99.6%

Length

2022-04-03T11:39:23.367905image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-03T11:39:23.415776image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
17.76889801025391
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

1986
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)100.0%
Missing265
Missing (%)99.6%
Memory size10.5 KiB
16.7716026306152

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row16.7716026306152

Common Values

ValueCountFrequency (%)
16.77160263061521
 
0.4%
(Missing)265
99.6%

Length

2022-04-03T11:39:23.739625image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-03T11:39:23.802428image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
16.77160263061521
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

1987
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)100.0%
Missing265
Missing (%)99.6%
Memory size10.5 KiB
11.2920875549316

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row11.2920875549316

Common Values

ValueCountFrequency (%)
11.29208755493161
 
0.4%
(Missing)265
99.6%

Length

2022-04-03T11:39:23.866278image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-03T11:39:23.926135image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
11.29208755493161
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

1988
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)100.0%
Missing265
Missing (%)99.6%
Memory size10.5 KiB
12.3102493286133

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row12.3102493286133

Common Values

ValueCountFrequency (%)
12.31024932861331
 
0.4%
(Missing)265
99.6%

Length

2022-04-03T11:39:23.986934image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-03T11:39:24.048768image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
12.31024932861331
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

1989
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing266
Missing (%)100.0%
Memory size2.2 KiB

1990
Categorical

MISSING
UNIFORM

Distinct3
Distinct (%)100.0%
Missing263
Missing (%)98.9%
Memory size10.6 KiB
7.0368332862854
8.11814880371094
1.43641090393066

Length

Max length16
Median length16
Mean length15.66666667
Min length15

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st row7.0368332862854
2nd row8.11814880371094
3rd row1.43641090393066

Common Values

ValueCountFrequency (%)
7.03683328628541
 
0.4%
8.118148803710941
 
0.4%
1.436410903930661
 
0.4%
(Missing)263
98.9%

Length

2022-04-03T11:39:24.118582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-03T11:39:24.188976image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
7.03683328628541
33.3%
8.118148803710941
33.3%
1.436410903930661
33.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

1991
Categorical

MISSING
UNIFORM

Distinct2
Distinct (%)100.0%
Missing264
Missing (%)99.2%
Memory size10.6 KiB
4.7332010269165
10.1180438995361

Length

Max length16
Median length15.5
Mean length15.5
Min length15

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)100.0%

Sample

1st row4.7332010269165
2nd row10.1180438995361

Common Values

ValueCountFrequency (%)
4.73320102691651
 
0.4%
10.11804389953611
 
0.4%
(Missing)264
99.2%

Length

2022-04-03T11:39:24.543685image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-03T11:39:24.606520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
4.73320102691651
50.0%
10.11804389953611
50.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

1992
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct4
Distinct (%)100.0%
Missing262
Missing (%)98.5%
Memory size10.6 KiB
13.2431535720825
1.25979006290436
9.69547748565674
19.7999992370605

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st row13.2431535720825
2nd row1.25979006290436
3rd row9.69547748565674
4th row19.7999992370605

Common Values

ValueCountFrequency (%)
13.24315357208251
 
0.4%
1.259790062904361
 
0.4%
9.695477485656741
 
0.4%
19.79999923706051
 
0.4%
(Missing)262
98.5%

Length

2022-04-03T11:39:24.676752image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-03T11:39:24.741606image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
13.24315357208251
25.0%
1.259790062904361
25.0%
9.695477485656741
25.0%
19.79999923706051
25.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

1993
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct4
Distinct (%)100.0%
Missing262
Missing (%)98.5%
Memory size10.6 KiB
8.54257297515869
4.54916000366211
9.27417469024658
16.888671875

Length

Max length16
Median length16
Mean length15
Min length12

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st row8.54257297515869
2nd row4.54916000366211
3rd row9.27417469024658
4th row16.888671875

Common Values

ValueCountFrequency (%)
8.542572975158691
 
0.4%
4.549160003662111
 
0.4%
9.274174690246581
 
0.4%
16.8886718751
 
0.4%
(Missing)262
98.5%

Length

2022-04-03T11:39:24.830738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-03T11:39:24.903578image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
8.542572975158691
25.0%
4.549160003662111
25.0%
9.274174690246581
25.0%
16.8886718751
25.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

1994
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct3
Distinct (%)100.0%
Missing263
Missing (%)98.9%
Memory size10.6 KiB
7.01745748519897
15.1315498352051
6.66898775100708

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st row7.01745748519897
2nd row15.1315498352051
3rd row6.66898775100708

Common Values

ValueCountFrequency (%)
7.017457485198971
 
0.4%
15.13154983520511
 
0.4%
6.668987751007081
 
0.4%
(Missing)263
98.9%

Length

2022-04-03T11:39:24.981833image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-03T11:39:25.044666image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
7.017457485198971
33.3%
15.13154983520511
33.3%
6.668987751007081
33.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

1995
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct10
Distinct (%)100.0%
Missing256
Missing (%)96.2%
Infinite0
Infinite (%)0.0%
Mean6.632519901
Minimum0.408460021
Maximum16.32642174
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:39:25.110486image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.408460021
5-th percentile1.165913755
Q13.483046293
median5.709706545
Q37.22286737
95-th percentile15.22294121
Maximum16.32642174
Range15.91796172
Interquartile range (IQR)3.739821076

Descriptive statistics

Standard deviation4.990821039
Coefficient of variation (CV)0.7524773561
Kurtosis0.4765451671
Mean6.632519901
Median Absolute Deviation (MAD)2.145789385
Skewness1.007754417
Sum66.32519901
Variance24.90829465
MonotonicityNot monotonic
2022-04-03T11:39:25.202216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2.091690541
 
0.4%
5.786954881
 
0.4%
3.0088815691
 
0.4%
0.4084600211
 
0.4%
7.3004603391
 
0.4%
6.9900884631
 
0.4%
4.9055404661
 
0.4%
5.632458211
 
0.4%
16.326421741
 
0.4%
13.874242781
 
0.4%
(Missing)256
96.2%
ValueCountFrequency (%)
0.4084600211
0.4%
2.091690541
0.4%
3.0088815691
0.4%
4.9055404661
0.4%
5.632458211
0.4%
5.786954881
0.4%
6.9900884631
0.4%
7.3004603391
0.4%
13.874242781
0.4%
16.326421741
0.4%
ValueCountFrequency (%)
16.326421741
0.4%
13.874242781
0.4%
7.3004603391
0.4%
6.9900884631
0.4%
5.786954881
0.4%
5.632458211
0.4%
4.9055404661
0.4%
3.0088815691
0.4%
2.091690541
0.4%
0.4084600211
0.4%

1996
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct16
Distinct (%)100.0%
Missing250
Missing (%)94.0%
Infinite0
Infinite (%)0.0%
Mean8.839913681
Minimum1.909424782
Maximum23.81573868
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:39:25.302974image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.909424782
5-th percentile3.436564982
Q15.387292266
median6.386861563
Q312.41900635
95-th percentile18.25404596
Maximum23.81573868
Range21.9063139
Interquartile range (IQR)7.031714082

Descriptive statistics

Standard deviation5.721941049
Coefficient of variation (CV)0.6472847196
Kurtosis1.764033958
Mean8.839913681
Median Absolute Deviation (MAD)1.643340111
Skewness1.385627583
Sum141.4386189
Variance32.74060936
MonotonicityNot monotonic
2022-04-03T11:39:25.388744image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
6.7867145541
 
0.4%
5.9441776281
 
0.4%
8.1393709181
 
0.4%
14.61791421
 
0.4%
5.9870085721
 
0.4%
5.5288867951
 
0.4%
4.8526906971
 
0.4%
1.9094247821
 
0.4%
7.386457921
 
0.4%
5.8145914081
 
0.4%
Other values (6)6
 
2.3%
(Missing)250
94.0%
ValueCountFrequency (%)
1.9094247821
0.4%
3.9456117151
0.4%
4.8526906971
0.4%
4.9625086781
0.4%
5.5288867951
0.4%
5.8145914081
0.4%
5.9441776281
0.4%
5.9870085721
0.4%
6.7867145541
0.4%
7.386457921
0.4%
ValueCountFrequency (%)
23.815738681
0.4%
16.400148391
0.4%
14.61791421
0.4%
13.183048251
0.4%
12.164325711
0.4%
8.1393709181
0.4%
7.386457921
0.4%
6.7867145541
0.4%
5.9870085721
0.4%
5.9441776281
0.4%

1997
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct15
Distinct (%)100.0%
Missing251
Missing (%)94.4%
Infinite0
Infinite (%)0.0%
Mean6.999663393
Minimum1.69039762
Maximum21.30848312
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:39:25.476509image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.69039762
5-th percentile1.744721711
Q13.357393146
median6.025568962
Q38.719809532
95-th percentile16.33335257
Maximum21.30848312
Range19.6180855
Interquartile range (IQR)5.362416387

Descriptive statistics

Standard deviation5.324087173
Coefficient of variation (CV)0.7606204576
Kurtosis2.696785932
Mean6.999663393
Median Absolute Deviation (MAD)2.851578712
Skewness1.524216952
Sum104.9949509
Variance28.34590423
MonotonicityNot monotonic
2022-04-03T11:39:25.571256image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1.7680034641
 
0.4%
21.308483121
 
0.4%
2.3184175491
 
0.4%
7.6564021111
 
0.4%
8.0034713751
 
0.4%
8.1852693561
 
0.4%
3.5407960411
 
0.4%
9.9373388291
 
0.4%
1.690397621
 
0.4%
4.3186964991
 
0.4%
Other values (5)5
 
1.9%
(Missing)251
94.4%
ValueCountFrequency (%)
1.690397621
0.4%
1.7680034641
0.4%
2.3184175491
0.4%
3.173990251
0.4%
3.5407960411
0.4%
3.6126122471
0.4%
4.3186964991
0.4%
6.0255689621
0.4%
7.6564021111
0.4%
8.0034713751
0.4%
ValueCountFrequency (%)
21.308483121
0.4%
14.201153761
0.4%
9.9373388291
0.4%
9.2543497091
0.4%
8.1852693561
0.4%
8.0034713751
0.4%
7.6564021111
0.4%
6.0255689621
0.4%
4.3186964991
0.4%
3.6126122471
0.4%

1998
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct21
Distinct (%)100.0%
Missing245
Missing (%)92.1%
Infinite0
Infinite (%)0.0%
Mean7.151073564
Minimum1.161943555
Maximum19.77118111
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:39:25.674979image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.161943555
5-th percentile1.41986227
Q15.09101963
median6.03292942
Q37.956717968
95-th percentile14.19999981
Maximum19.77118111
Range18.60923755
Interquartile range (IQR)2.865698338

Descriptive statistics

Standard deviation4.474409583
Coefficient of variation (CV)0.6256976023
Kurtosis1.943228488
Mean7.151073564
Median Absolute Deviation (MAD)1.880703926
Skewness1.216277971
Sum150.1725448
Variance20.02034112
MonotonicityNot monotonic
2022-04-03T11:39:25.773714image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2.3584318161
 
0.4%
14.199999811
 
0.4%
5.991489411
 
0.4%
5.1680316931
 
0.4%
2.870911361
 
0.4%
5.8727140431
 
0.4%
11.001970291
 
0.4%
5.091019631
 
0.4%
12.347995761
 
0.4%
1.419862271
 
0.4%
Other values (11)11
 
4.1%
(Missing)245
92.1%
ValueCountFrequency (%)
1.1619435551
0.4%
1.419862271
0.4%
2.3584318161
0.4%
2.870911361
0.4%
4.1522254941
0.4%
5.091019631
0.4%
5.1680316931
0.4%
5.8727140431
0.4%
5.966802121
0.4%
5.991489411
0.4%
ValueCountFrequency (%)
19.771181111
0.4%
14.199999811
0.4%
12.347995761
0.4%
11.001970291
0.4%
10.983981131
0.4%
7.9567179681
0.4%
7.5213713651
0.4%
7.4194183351
0.4%
6.7972607611
0.4%
6.0862874981
0.4%

1999
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct23
Distinct (%)100.0%
Missing243
Missing (%)91.4%
Infinite0
Infinite (%)0.0%
Mean7.601233197
Minimum0.5150088072
Maximum44.81721115
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:39:25.878000image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.5150088072
5-th percentile0.6450349331
Q11.800114393
median6.930771351
Q38.718099594
95-th percentile15.00688877
Maximum44.81721115
Range44.30220234
Interquartile range (IQR)6.917985201

Descriptive statistics

Standard deviation9.168016017
Coefficient of variation (CV)1.206122188
Kurtosis12.91608348
Mean7.601233197
Median Absolute Deviation (MAD)4.89978838
Skewness3.238569744
Sum174.8283635
Variance84.0525177
MonotonicityNot monotonic
2022-04-03T11:39:25.976768image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
15.353122711
 
0.4%
9.434899331
 
0.4%
11.890783311
 
0.4%
2.0782890321
 
0.4%
6.9307713511
 
0.4%
11.868757251
 
0.4%
0.62709975241
 
0.4%
1.4629472491
 
0.4%
7.4384222031
 
0.4%
7.9942955971
 
0.4%
Other values (13)13
 
4.9%
(Missing)243
91.4%
ValueCountFrequency (%)
0.51500880721
0.4%
0.62709975241
0.4%
0.80645155911
0.4%
1.4629472491
0.4%
1.4856332541
0.4%
1.5788438321
0.4%
2.0213849541
0.4%
2.0782890321
0.4%
3.897430421
0.4%
4.3107366561
0.4%
ValueCountFrequency (%)
44.817211151
0.4%
15.353122711
0.4%
11.890783311
0.4%
11.868757251
0.4%
11.830559731
0.4%
9.434899331
0.4%
8.0012998581
0.4%
7.9942955971
0.4%
7.4724717141
0.4%
7.4384222031
0.4%

2000
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct43
Distinct (%)100.0%
Missing223
Missing (%)83.8%
Infinite0
Infinite (%)0.0%
Mean8.049315785
Minimum1.370965362
Maximum20.79124641
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:39:26.090497image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.370965362
5-th percentile1.986838496
Q15.35153532
median8.011335373
Q310.56411266
95-th percentile14.87487812
Maximum20.79124641
Range19.42028105
Interquartile range (IQR)5.212577343

Descriptive statistics

Standard deviation4.31308977
Coefficient of variation (CV)0.5358330926
Kurtosis0.4670687415
Mean8.049315785
Median Absolute Deviation (MAD)2.937328815
Skewness0.5651026053
Sum346.1205788
Variance18.60274337
MonotonicityNot monotonic
2022-04-03T11:39:26.213168image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
14.846263891
 
0.4%
1.9857591391
 
0.4%
5.6835298541
 
0.4%
11.81786061
 
0.4%
2.8326420781
 
0.4%
12.394744871
 
0.4%
9.2332916261
 
0.4%
7.0279278761
 
0.4%
3.2151789671
 
0.4%
8.440650941
 
0.4%
Other values (33)33
 
12.4%
(Missing)223
83.8%
ValueCountFrequency (%)
1.3709653621
0.4%
1.8272180561
0.4%
1.9857591391
0.4%
1.9965527061
0.4%
2.3638920781
0.4%
2.7000000481
0.4%
2.8326420781
0.4%
3.2151789671
0.4%
3.2999999521
0.4%
3.6828551291
0.4%
ValueCountFrequency (%)
20.791246411
0.4%
15.414498331
0.4%
14.878057481
0.4%
14.846263891
0.4%
14.036120411
0.4%
12.394744871
0.4%
12.379811291
0.4%
11.81786061
0.4%
11.50406171
0.4%
11.213946341
0.4%

2001
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct28
Distinct (%)100.0%
Missing238
Missing (%)89.5%
Infinite0
Infinite (%)0.0%
Mean7.595331264
Minimum1.600012898
Maximum15.94995308
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:39:26.331416image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.600012898
5-th percentile2.013980198
Q12.878745556
median7.141815424
Q311.61227894
95-th percentile14.59231958
Maximum15.94995308
Range14.34994018
Interquartile range (IQR)8.733533382

Descriptive statistics

Standard deviation4.456597618
Coefficient of variation (CV)0.5867548712
Kurtosis-1.138925728
Mean7.595331264
Median Absolute Deviation (MAD)4.334959745
Skewness0.3026214045
Sum212.6692754
Variance19.86126233
MonotonicityNot monotonic
2022-04-03T11:39:26.437666image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
5.3875565531
 
0.4%
2.5884547231
 
0.4%
1.6000128981
 
0.4%
1.9311034681
 
0.4%
7.0031461721
 
0.4%
10.318799971
 
0.4%
12.398313521
 
0.4%
5.9556450841
 
0.4%
15.949953081
 
0.4%
8.7571210861
 
0.4%
Other values (18)18
 
6.8%
(Missing)238
89.5%
ValueCountFrequency (%)
1.6000128981
0.4%
1.9311034681
0.4%
2.1678941251
0.4%
2.5648982521
0.4%
2.5884547231
0.4%
2.6813280581
0.4%
2.8149819371
0.4%
2.9000000951
0.4%
4.0999999051
0.4%
5.3875565531
0.4%
ValueCountFrequency (%)
15.949953081
0.4%
14.856602671
0.4%
14.101508141
0.4%
13.949630741
0.4%
12.398313521
0.4%
12.243350031
0.4%
11.994411471
0.4%
11.484901431
0.4%
10.318799971
0.4%
8.8088045121
0.4%

2002
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct36
Distinct (%)100.0%
Missing230
Missing (%)86.5%
Infinite0
Infinite (%)0.0%
Mean7.66854938
Minimum0.8265140653
Maximum23.36560631
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:39:26.555887image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.8265140653
5-th percentile1.248491585
Q13.138298929
median7.062778234
Q310.65249062
95-th percentile14.94250798
Maximum23.36560631
Range22.53909224
Interquartile range (IQR)7.514191687

Descriptive statistics

Standard deviation5.102813696
Coefficient of variation (CV)0.6654209868
Kurtosis1.224511886
Mean7.66854938
Median Absolute Deviation (MAD)3.946950316
Skewness0.9089703033
Sum276.0677777
Variance26.03870761
MonotonicityNot monotonic
2022-04-03T11:39:26.668714image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
2.4011545181
 
0.4%
12.082244871
 
0.4%
9.1928672791
 
0.4%
9.5830078121
 
0.4%
6.5785102841
 
0.4%
1.2845041751
 
0.4%
0.82651406531
 
0.4%
12.294426921
 
0.4%
7.6072864531
 
0.4%
3.1607699391
 
0.4%
Other values (26)26
 
9.8%
(Missing)230
86.5%
ValueCountFrequency (%)
0.82651406531
0.4%
1.1404538151
0.4%
1.2845041751
0.4%
1.4778475761
0.4%
2.118990661
0.4%
2.2939300541
0.4%
2.4011545181
0.4%
31
0.4%
3.0708858971
0.4%
3.1607699391
0.4%
ValueCountFrequency (%)
23.365606311
0.4%
18.469686511
0.4%
13.766781811
0.4%
12.719157221
0.4%
12.407626151
0.4%
12.294426921
0.4%
12.272295951
0.4%
12.082244871
0.4%
11.474639891
0.4%
10.378440861
0.4%

2003
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct37
Distinct (%)100.0%
Missing229
Missing (%)86.1%
Infinite0
Infinite (%)0.0%
Mean9.075182087
Minimum0.4299614429
Maximum35.93376923
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:39:26.783898image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.4299614429
5-th percentile1.383254957
Q15.376136303
median8.326233864
Q311.10616875
95-th percentile18.21117058
Maximum35.93376923
Range35.50380778
Interquartile range (IQR)5.730032444

Descriptive statistics

Standard deviation6.467526489
Coefficient of variation (CV)0.7126607959
Kurtosis7.643022881
Mean9.075182087
Median Absolute Deviation (MAD)2.950097561
Skewness2.185045304
Sum335.7817372
Variance41.82889889
MonotonicityNot monotonic
2022-04-03T11:39:26.902084image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
35.933769231
 
0.4%
22.850172041
 
0.4%
9.9702854161
 
0.4%
11.929939271
 
0.4%
2.9372682571
 
0.4%
11.728190421
 
0.4%
7.9501256941
 
0.4%
1.409370781
 
0.4%
7.7537140851
 
0.4%
10.607320791
 
0.4%
Other values (27)27
 
10.2%
(Missing)229
86.1%
ValueCountFrequency (%)
0.42996144291
0.4%
1.2787916661
0.4%
1.409370781
0.4%
2.3170692921
0.4%
2.9372682571
0.4%
3.2999999521
0.4%
41
0.4%
4.7293252941
0.4%
5.2763624191
0.4%
5.3761363031
0.4%
ValueCountFrequency (%)
35.933769231
0.4%
22.850172041
0.4%
17.051420211
0.4%
15.413519861
0.4%
12.368428231
0.4%
12.048434261
0.4%
11.929939271
0.4%
11.728190421
0.4%
11.511560441
0.4%
11.106168751
0.4%

2004
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct40
Distinct (%)100.0%
Missing226
Missing (%)85.0%
Infinite0
Infinite (%)0.0%
Mean7.546796668
Minimum0.312082231
Maximum16.90258408
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:39:27.022068image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.312082231
5-th percentile1.122924498
Q13.353303492
median7.479361773
Q310.38220406
95-th percentile15.93418913
Maximum16.90258408
Range16.59050184
Interquartile range (IQR)7.028900564

Descriptive statistics

Standard deviation4.505943541
Coefficient of variation (CV)0.5970670391
Kurtosis-0.7158071959
Mean7.546796668
Median Absolute Deviation (MAD)3.872197032
Skewness0.3144853759
Sum301.8718667
Variance20.30352719
MonotonicityNot monotonic
2022-04-03T11:39:27.141006image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
11.984016421
 
0.4%
9.4413709641
 
0.4%
7.330427171
 
0.4%
7.1869449621
 
0.4%
10.537411691
 
0.4%
0.3120822311
 
0.4%
3.3710713391
 
0.4%
12.737663271
 
0.4%
7.9112691881
 
0.4%
9.6196451191
 
0.4%
Other values (30)30
 
11.3%
(Missing)226
85.0%
ValueCountFrequency (%)
0.3120822311
0.4%
0.81836313011
0.4%
1.1389540431
0.4%
2.0626106261
0.4%
2.394564391
0.4%
2.4387252331
0.4%
2.6290194991
0.4%
2.7931430341
0.4%
3.2000000481
0.4%
3.2999999521
0.4%
ValueCountFrequency (%)
16.902584081
0.4%
16.455030441
0.4%
15.906776431
0.4%
14.560505871
0.4%
12.737663271
0.4%
12.084314351
0.4%
11.984016421
0.4%
11.76487161
0.4%
11.570426941
0.4%
10.537411691
0.4%

2005
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct57
Distinct (%)100.0%
Missing209
Missing (%)78.6%
Infinite0
Infinite (%)0.0%
Mean8.493747662
Minimum0.2127556354
Maximum21.90860939
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:39:27.274303image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.2127556354
5-th percentile1.744485927
Q15.102686405
median8.204703331
Q312.03623199
95-th percentile16.22555389
Maximum21.90860939
Range21.69585375
Interquartile range (IQR)6.933545589

Descriptive statistics

Standard deviation5.017200608
Coefficient of variation (CV)0.5906933909
Kurtosis-0.05580184744
Mean8.493747662
Median Absolute Deviation (MAD)3.591929913
Skewness0.5297326281
Sum484.1436168
Variance25.17230194
MonotonicityNot monotonic
2022-04-03T11:39:27.399995image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.830271721
 
0.4%
0.21275563541
 
0.4%
10.7967311
 
0.4%
7.0613412861
 
0.4%
8.566647531
 
0.4%
5.4740066531
 
0.4%
3.7427577971
 
0.4%
14.613136291
 
0.4%
6.2466721531
 
0.4%
10.061977391
 
0.4%
Other values (47)47
 
17.7%
(Missing)209
78.6%
ValueCountFrequency (%)
0.21275563541
0.4%
1.3846504691
0.4%
1.703645111
0.4%
1.7546961311
0.4%
2.0268921851
0.4%
2.1394436361
0.4%
2.2999999521
0.4%
2.3546032911
0.4%
2.5831332211
0.4%
3.1127748491
0.4%
ValueCountFrequency (%)
21.908609391
0.4%
20.29677011
0.4%
19.514019011
0.4%
15.403437611
0.4%
15.061282161
0.4%
15.035944941
0.4%
14.613136291
0.4%
13.621863371
0.4%
12.734930041
0.4%
12.689409261
0.4%

2006
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct41
Distinct (%)100.0%
Missing225
Missing (%)84.6%
Infinite0
Infinite (%)0.0%
Mean8.034884718
Minimum0
Maximum22.00154877
Zeros1
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:39:27.524152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.822532654
Q14
median7.145264626
Q310.83256435
95-th percentile16.29310226
Maximum22.00154877
Range22.00154877
Interquartile range (IQR)6.832564354

Descriptive statistics

Standard deviation4.988806923
Coefficient of variation (CV)0.620893404
Kurtosis0.09993549533
Mean8.034884718
Median Absolute Deviation (MAD)3.556447983
Skewness0.6288277853
Sum329.4302734
Variance24.88819452
MonotonicityNot monotonic
2022-04-03T11:39:27.642836image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
10.283141141
 
0.4%
3.3753757481
 
0.4%
6.5002450941
 
0.4%
4.9393305781
 
0.4%
10.74095441
 
0.4%
4.3936977391
 
0.4%
13.815055851
 
0.4%
7.1452646261
 
0.4%
10.618554121
 
0.4%
3.7630980011
 
0.4%
Other values (31)31
 
11.7%
(Missing)225
84.6%
ValueCountFrequency (%)
01
0.4%
0.06870139391
0.4%
1.8225326541
0.4%
1.8549101351
0.4%
2.9000000951
0.4%
3.2915208341
0.4%
3.3753757481
0.4%
3.5888166431
0.4%
3.7630980011
0.4%
3.9600162511
0.4%
ValueCountFrequency (%)
22.001548771
0.4%
16.836744311
0.4%
16.293102261
0.4%
15.057058331
0.4%
14.581669811
0.4%
14.408937451
0.4%
13.815055851
0.4%
12.456750871
0.4%
11.319570541
0.4%
10.914824491
0.4%

2007
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct43
Distinct (%)100.0%
Missing223
Missing (%)83.8%
Infinite0
Infinite (%)0.0%
Mean7.725381358
Minimum1.621716976
Maximum18.44475365
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:39:27.761516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.621716976
5-th percentile2.247376633
Q13.299010277
median6.87981081
Q311.4756279
95-th percentile17.43022814
Maximum18.44475365
Range16.82303667
Interquartile range (IQR)8.176617622

Descriptive statistics

Standard deviation4.94525804
Coefficient of variation (CV)0.6401312519
Kurtosis-0.791758402
Mean7.725381358
Median Absolute Deviation (MAD)3.897488594
Skewness0.5913780252
Sum332.1913984
Variance24.45557708
MonotonicityNot monotonic
2022-04-03T11:39:27.883163image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
4.8375339511
 
0.4%
7.8584856991
 
0.4%
15.372239111
 
0.4%
14.453977581
 
0.4%
7.6439671521
 
0.4%
1.6217169761
 
0.4%
2.709297181
 
0.4%
3.3121137621
 
0.4%
14.181655881
 
0.4%
9.3869161611
 
0.4%
Other values (33)33
 
12.4%
(Missing)223
83.8%
ValueCountFrequency (%)
1.6217169761
0.4%
2.0469973091
0.4%
2.2353160381
0.4%
2.3559219841
0.4%
2.5388102531
0.4%
2.631760121
0.4%
2.6371278761
0.4%
2.709297181
0.4%
2.9823222161
0.4%
3.1402680871
0.4%
ValueCountFrequency (%)
18.444753651
0.4%
17.744073871
0.4%
17.658893591
0.4%
15.372239111
0.4%
14.453977581
0.4%
14.181655881
0.4%
12.97608281
0.4%
12.760241511
0.4%
12.220600131
0.4%
11.834750181
0.4%

2008
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct38
Distinct (%)100.0%
Missing228
Missing (%)85.7%
Infinite0
Infinite (%)0.0%
Mean8.888857096
Minimum0.7817690969
Maximum22.12051964
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:39:28.011846image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.7817690969
5-th percentile1.640680873
Q13.686253905
median7.560149431
Q313.64263082
95-th percentile17.82890282
Maximum22.12051964
Range21.33875054
Interquartile range (IQR)9.95637691

Descriptive statistics

Standard deviation5.789643761
Coefficient of variation (CV)0.6513372527
Kurtosis-0.9223862687
Mean8.888857096
Median Absolute Deviation (MAD)4.604812741
Skewness0.4127409532
Sum337.7765697
Variance33.51997488
MonotonicityNot monotonic
2022-04-03T11:39:28.125542image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
14.004098891
 
0.4%
14.929262161
 
0.4%
3.5864200591
 
0.4%
16.202207571
 
0.4%
6.8561329841
 
0.4%
1.1935632231
 
0.4%
3.0868356231
 
0.4%
11.573938371
 
0.4%
11.475711821
 
0.4%
12.850165371
 
0.4%
Other values (28)28
 
10.5%
(Missing)228
85.7%
ValueCountFrequency (%)
0.78176909691
0.4%
1.1935632231
0.4%
1.7195839881
0.4%
2.2738981251
0.4%
2.9368476871
0.4%
2.9738256931
0.4%
3.0868356231
0.4%
3.5864200591
0.4%
3.6542270181
0.4%
3.6691393851
0.4%
ValueCountFrequency (%)
22.120519641
0.4%
20.011768341
0.4%
17.443691251
0.4%
16.202207571
0.4%
15.095375061
0.4%
15.070994381
0.4%
14.929262161
0.4%
14.552155491
0.4%
14.004098891
0.4%
13.906785961
0.4%

2009
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct46
Distinct (%)100.0%
Missing220
Missing (%)82.7%
Infinite0
Infinite (%)0.0%
Mean9.467608563
Minimum1.003367305
Maximum26.05967522
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:39:28.247216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.003367305
5-th percentile1.963408619
Q14.07609278
median8.420957088
Q314.28783488
95-th percentile19.57339716
Maximum26.05967522
Range25.05630791
Interquartile range (IQR)10.2117421

Descriptive statistics

Standard deviation6.477858283
Coefficient of variation (CV)0.6842127281
Kurtosis-0.3098307738
Mean9.467608563
Median Absolute Deviation (MAD)4.923430443
Skewness0.6974202419
Sum435.5099939
Variance41.96264794
MonotonicityNot monotonic
2022-04-03T11:39:28.373878image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
18.356817251
 
0.4%
1.8400782351
 
0.4%
1.2160114051
 
0.4%
24.769697191
 
0.4%
16.175725941
 
0.4%
10.636750221
 
0.4%
4.4985117911
 
0.4%
14.395202641
 
0.4%
8.8860254291
 
0.4%
12.147171021
 
0.4%
Other values (36)36
 
13.5%
(Missing)220
82.7%
ValueCountFrequency (%)
1.0033673051
0.4%
1.2160114051
0.4%
1.8400782351
0.4%
2.3333997731
0.4%
2.4635674951
0.4%
2.6471064091
0.4%
3.2245700361
0.4%
3.4950532911
0.4%
3.51
0.4%
3.5221343041
0.4%
ValueCountFrequency (%)
26.059675221
0.4%
24.769697191
0.4%
19.849660871
0.4%
18.744606021
0.4%
18.356817251
0.4%
16.772417071
0.4%
16.53252221
0.4%
16.175725941
0.4%
16.05152131
0.4%
15.463336941
0.4%

2010
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct63
Distinct (%)100.0%
Missing203
Missing (%)76.3%
Infinite0
Infinite (%)0.0%
Mean8.03102514
Minimum0.2879007161
Maximum24.95895767
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:39:28.497584image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.2879007161
5-th percentile0.9101337492
Q13.377308369
median7.028286934
Q312.52022409
95-th percentile17.48577099
Maximum24.95895767
Range24.67105696
Interquartile range (IQR)9.142915726

Descriptive statistics

Standard deviation5.637441904
Coefficient of variation (CV)0.7019579451
Kurtosis-0.1791495888
Mean8.03102514
Median Absolute Deviation (MAD)4.266716003
Skewness0.6368206247
Sum505.9545838
Variance31.78075122
MonotonicityNot monotonic
2022-04-03T11:39:28.889313image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.519723891
 
0.4%
18.105762481
 
0.4%
1.6378482581
 
0.4%
11.463217741
 
0.4%
7.0282869341
 
0.4%
1.3326200251
 
0.4%
1.6445199251
 
0.4%
4.6610345841
 
0.4%
18.875629431
 
0.4%
3.0383996961
 
0.4%
Other values (53)53
 
19.9%
(Missing)203
76.3%
ValueCountFrequency (%)
0.28790071611
0.4%
0.60780876871
0.4%
0.75069129471
0.4%
0.86319082981
0.4%
1.3326200251
0.4%
1.5040348771
0.4%
1.528465391
0.4%
1.6378482581
0.4%
1.6445199251
0.4%
1.7300095561
0.4%
ValueCountFrequency (%)
24.958957671
0.4%
18.875629431
0.4%
18.105762481
0.4%
17.519723891
0.4%
17.180194851
0.4%
16.693086621
0.4%
14.636776921
0.4%
14.22404481
0.4%
14.166285511
0.4%
14.012608531
0.4%

2011
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct39
Distinct (%)100.0%
Missing227
Missing (%)85.3%
Infinite0
Infinite (%)0.0%
Mean9.826707522
Minimum1.751371264
Maximum37.09108353
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:39:29.010982image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.751371264
5-th percentile1.873665607
Q14.247376561
median8.558734894
Q313.45748758
95-th percentile19.13296604
Maximum37.09108353
Range35.33971226
Interquartile range (IQR)9.210111022

Descriptive statistics

Standard deviation7.318473438
Coefficient of variation (CV)0.7447533593
Kurtosis3.710638224
Mean9.826707522
Median Absolute Deviation (MAD)4.644614935
Skewness1.57493268
Sum383.2415934
Variance53.56005346
MonotonicityNot monotonic
2022-04-03T11:39:29.124684image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
12.795637131
 
0.4%
1.7513712641
 
0.4%
6.6261792181
 
0.4%
3.7557520871
 
0.4%
11.245654111
 
0.4%
13.89794351
 
0.4%
18.377571111
 
0.4%
13.017031671
 
0.4%
3.9141199591
 
0.4%
3.3269221781
 
0.4%
Other values (29)29
 
10.9%
(Missing)227
85.3%
ValueCountFrequency (%)
1.7513712641
0.4%
1.8255310061
0.4%
1.8790138961
0.4%
2.2039940361
0.4%
2.5632214551
0.4%
3.3269221781
0.4%
3.342090131
0.4%
3.7557520871
0.4%
3.8747925761
0.4%
3.9141199591
0.4%
ValueCountFrequency (%)
37.091083531
0.4%
23.971628191
0.4%
18.595336911
0.4%
18.470733641
0.4%
18.377571111
0.4%
17.911615371
0.4%
17.555883411
0.4%
15.630519871
0.4%
14.121216771
0.4%
13.89794351
0.4%

2012
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct44
Distinct (%)100.0%
Missing222
Missing (%)83.5%
Infinite0
Infinite (%)0.0%
Mean8.908854298
Minimum1.475644588
Maximum26.60000038
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:39:29.242372image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.475644588
5-th percentile2.172456229
Q13.673658073
median6.624903917
Q312.72172451
95-th percentile22.47728863
Maximum26.60000038
Range25.12435579
Interquartile range (IQR)9.048066437

Descriptive statistics

Standard deviation6.539742518
Coefficient of variation (CV)0.7340722274
Kurtosis0.765409094
Mean8.908854298
Median Absolute Deviation (MAD)3.665875554
Skewness1.138994393
Sum391.9895891
Variance42.7682322
MonotonicityNot monotonic
2022-04-03T11:39:29.369002image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
5.7820448881
 
0.4%
17.772525791
 
0.4%
16.308698651
 
0.4%
2.8698730471
 
0.4%
2.5079259871
 
0.4%
7.7828941351
 
0.4%
2.1581897741
 
0.4%
5.59144641
 
0.4%
16.876607891
 
0.4%
11.712320331
 
0.4%
Other values (34)34
 
12.8%
(Missing)222
83.5%
ValueCountFrequency (%)
1.4756445881
0.4%
1.9057523011
0.4%
2.1581897741
0.4%
2.2532994751
0.4%
2.5079259871
0.4%
2.5206470491
0.4%
2.8698730471
0.4%
3.048183681
0.4%
3.1010980611
0.4%
3.4461007121
0.4%
ValueCountFrequency (%)
26.600000381
0.4%
26.201841351
0.4%
23.307540891
0.4%
17.772525791
0.4%
16.876607891
0.4%
16.724653241
0.4%
16.308698651
0.4%
15.735779761
0.4%
13.92779351
0.4%
13.896782881
0.4%

2013
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct40
Distinct (%)100.0%
Missing226
Missing (%)85.0%
Infinite0
Infinite (%)0.0%
Mean8.875244746
Minimum1.100000024
Maximum24.61247063
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:39:29.487683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.100000024
5-th percentile1.636886585
Q13.716330051
median8.29034996
Q312.36786056
95-th percentile18.90893879
Maximum24.61247063
Range23.5124706
Interquartile range (IQR)8.651530504

Descriptive statistics

Standard deviation5.875625453
Coefficient of variation (CV)0.6620240479
Kurtosis-0.02957749211
Mean8.875244746
Median Absolute Deviation (MAD)4.4012568
Skewness0.7134072419
Sum355.0097898
Variance34.52297446
MonotonicityNot monotonic
2022-04-03T11:39:29.612378image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
9.1323118211
 
0.4%
1.1000000241
 
0.4%
10.312828061
 
0.4%
5.6103663441
 
0.4%
10.486863141
 
0.4%
13.291493421
 
0.4%
6.8833169941
 
0.4%
12.547323231
 
0.4%
4.8350834851
 
0.4%
1.6322159771
 
0.4%
Other values (30)30
 
11.3%
(Missing)226
85.0%
ValueCountFrequency (%)
1.1000000241
0.4%
1.6322159771
0.4%
1.6371324061
0.4%
1.7300000191
0.4%
1.8065929411
0.4%
2.203418971
0.4%
2.5591628551
0.4%
3.2879281041
0.4%
3.3318214421
0.4%
3.6308913231
0.4%
ValueCountFrequency (%)
24.612470631
0.4%
20.536521911
0.4%
18.823276521
0.4%
18.04095841
0.4%
16.051218031
0.4%
16.045223241
0.4%
15.81170751
0.4%
14.631330491
0.4%
13.291493421
0.4%
12.547323231
0.4%

2014
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct44
Distinct (%)100.0%
Missing222
Missing (%)83.5%
Infinite0
Infinite (%)0.0%
Mean8.414707942
Minimum1.027531385
Maximum24.73163414
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:39:29.733313image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.027531385
5-th percentile1.72819255
Q13.375713766
median7.428487778
Q311.84939551
95-th percentile19.38041792
Maximum24.73163414
Range23.70410275
Interquartile range (IQR)8.473681748

Descriptive statistics

Standard deviation5.989206918
Coefficient of variation (CV)0.71175458
Kurtosis0.2427744838
Mean8.414707942
Median Absolute Deviation (MAD)4.263630867
Skewness0.907062467
Sum370.2471495
Variance35.8705995
MonotonicityNot monotonic
2022-04-03T11:39:29.863935image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
3.4577629571
 
0.4%
3.0398809911
 
0.4%
1.6148692371
 
0.4%
11.656827931
 
0.4%
24.731634141
 
0.4%
10.202669141
 
0.4%
9.1743574141
 
0.4%
12.613127711
 
0.4%
7.1014761921
 
0.4%
4.8707909581
 
0.4%
Other values (34)34
 
12.8%
(Missing)222
83.5%
ValueCountFrequency (%)
1.0275313851
0.4%
1.6148692371
0.4%
1.6917510031
0.4%
1.9346946481
0.4%
2.0344696041
0.4%
2.1376211641
0.4%
2.6123664381
0.4%
2.6126701831
0.4%
2.9672412871
0.4%
3.0398809911
0.4%
ValueCountFrequency (%)
24.731634141
0.4%
22.32789041
0.4%
19.474941251
0.4%
18.844785691
0.4%
16.335824971
0.4%
15.957111361
0.4%
15.825387951
0.4%
13.167323111
0.4%
12.853449821
0.4%
12.613127711
0.4%

2015
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct58
Distinct (%)100.0%
Missing208
Missing (%)78.2%
Infinite0
Infinite (%)0.0%
Mean9.692248712
Minimum0.03236065805
Maximum31.21570015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:39:29.992841image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.03236065805
5-th percentile1.82448948
Q14.315157056
median8.221334934
Q314.48214078
95-th percentile19.86790285
Maximum31.21570015
Range31.18333949
Interquartile range (IQR)10.16698372

Descriptive statistics

Standard deviation6.898955414
Coefficient of variation (CV)0.7118013187
Kurtosis0.874640285
Mean9.692248712
Median Absolute Deviation (MAD)5.398192883
Skewness0.9432515494
Sum562.1504253
Variance47.5955858
MonotonicityNot monotonic
2022-04-03T11:39:30.122067image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.732435231
 
0.4%
14.659899711
 
0.4%
2.1342997551
 
0.4%
15.940337181
 
0.4%
13.822774891
 
0.4%
10.268335341
 
0.4%
4.2874407771
 
0.4%
1.5189501051
 
0.4%
18.562143331
 
0.4%
9.8425655361
 
0.4%
Other values (48)48
 
18.0%
(Missing)208
78.2%
ValueCountFrequency (%)
0.032360658051
0.4%
0.19748863581
0.4%
1.5189501051
0.4%
1.8784081941
0.4%
2.1167721751
0.4%
2.1342997551
0.4%
2.466508151
0.4%
2.5134332181
0.4%
2.538545371
0.4%
2.6512396341
0.4%
ValueCountFrequency (%)
31.215700151
0.4%
29.007001881
0.4%
22.425754551
0.4%
19.416517261
0.4%
18.562143331
0.4%
18.48619081
0.4%
17.031723021
0.4%
16.736173631
0.4%
16.289146421
0.4%
16.089826581
0.4%

2016
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct51
Distinct (%)100.0%
Missing215
Missing (%)80.8%
Infinite0
Infinite (%)0.0%
Mean9.279666423
Minimum0.5474506021
Maximum24.42482948
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:39:30.255067image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.5474506021
5-th percentile1.43827647
Q13.917337418
median7.565099716
Q314.02890587
95-th percentile23.06404781
Maximum24.42482948
Range23.87737888
Interquartile range (IQR)10.11156845

Descriptive statistics

Standard deviation6.774231044
Coefficient of variation (CV)0.7300080343
Kurtosis-0.3724394074
Mean9.279666423
Median Absolute Deviation (MAD)4.165099621
Skewness0.7817165371
Sum473.2629876
Variance45.89020624
MonotonicityNot monotonic
2022-04-03T11:39:30.389734image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.841320041
 
0.4%
1.5510905981
 
0.4%
1.9529494051
 
0.4%
4.4852004051
 
0.4%
4.3807959561
 
0.4%
1.9600000381
 
0.4%
10.658334731
 
0.4%
8.4605798721
 
0.4%
14.112551691
 
0.4%
7.5650997161
 
0.4%
Other values (41)41
 
15.4%
(Missing)215
80.8%
ValueCountFrequency (%)
0.54745060211
0.4%
1.1505730151
0.4%
1.3254623411
0.4%
1.5510905981
0.4%
1.9529494051
0.4%
1.9600000381
0.4%
1.994661451
0.4%
2.0880463121
0.4%
2.3427677151
0.4%
3.1863703731
0.4%
ValueCountFrequency (%)
24.424829481
0.4%
23.979799271
0.4%
23.841320041
0.4%
22.286775591
0.4%
21.406967161
0.4%
19.287050251
0.4%
18.684209821
0.4%
16.893777851
0.4%
16.374492651
0.4%
15.776790621
0.4%

2017
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct42
Distinct (%)100.0%
Missing224
Missing (%)84.2%
Infinite0
Infinite (%)0.0%
Mean10.71830878
Minimum0.1328063309
Maximum31.20792007
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:39:30.519078image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1328063309
5-th percentile1.541901809
Q15.171670079
median9.206577301
Q315.12632251
95-th percentile21.14627838
Maximum31.20792007
Range31.07511374
Interquartile range (IQR)9.954652429

Descriptive statistics

Standard deviation7.182721141
Coefficient of variation (CV)0.67013568
Kurtosis1.352592478
Mean10.71830878
Median Absolute Deviation (MAD)4.756778955
Skewness1.031629061
Sum450.1689687
Variance51.591483
MonotonicityNot monotonic
2022-04-03T11:39:30.639477image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
16.451969151
 
0.4%
14.215151791
 
0.4%
15.430046081
 
0.4%
1.5074330571
 
0.4%
12.706737521
 
0.4%
8.2011241911
 
0.4%
4.3819384571
 
0.4%
6.2029719351
 
0.4%
7.9763927461
 
0.4%
0.13280633091
 
0.4%
Other values (32)32
 
12.0%
(Missing)224
84.2%
ValueCountFrequency (%)
0.13280633091
0.4%
1.4653545621
0.4%
1.5074330571
0.4%
2.19680811
0.4%
2.2200067041
0.4%
3.7999999521
0.4%
3.8342390061
0.4%
3.9846892361
0.4%
4.3819384571
0.4%
4.5176582341
0.4%
ValueCountFrequency (%)
31.207920071
0.4%
31.143743521
0.4%
21.155242921
0.4%
20.975952151
0.4%
17.569158551
0.4%
17.321882251
0.4%
16.857152941
0.4%
16.665388111
0.4%
16.451969151
0.4%
15.799092291
0.4%

2018
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct26
Distinct (%)100.0%
Missing240
Missing (%)90.2%
Infinite0
Infinite (%)0.0%
Mean9.645799123
Minimum1.309999943
Maximum35.52585983
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:39:30.756164image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.309999943
5-th percentile2.044335246
Q14.77146852
median7.975033522
Q311.53039217
95-th percentile20.20634413
Maximum35.52585983
Range34.21585989
Interquartile range (IQR)6.75892365

Descriptive statistics

Standard deviation7.321161762
Coefficient of variation (CV)0.7590000236
Kurtosis5.302031886
Mean9.645799123
Median Absolute Deviation (MAD)3.699156284
Skewness1.922418178
Sum250.7907772
Variance53.59940955
MonotonicityNot monotonic
2022-04-03T11:39:30.858889image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
7.1940727231
 
0.4%
7.7825903891
 
0.4%
4.2952914241
 
0.4%
10.250509261
 
0.4%
2.0387697221
 
0.4%
13.376516341
 
0.4%
3.6947484021
 
0.4%
16.402767181
 
0.4%
1.3099999431
 
0.4%
9.2154607771
 
0.4%
Other values (16)16
 
6.0%
(Missing)240
90.2%
ValueCountFrequency (%)
1.3099999431
0.4%
2.0387697221
0.4%
2.0610318181
0.4%
2.2744064331
0.4%
3.6947484021
0.4%
4.2564630511
0.4%
4.2952914241
0.4%
6.1999998091
0.4%
6.5163226131
0.4%
7.0636668211
0.4%
ValueCountFrequency (%)
35.525859831
0.4%
21.27512551
0.4%
171
0.4%
16.402767181
0.4%
15.794722561
0.4%
13.376516341
0.4%
11.957019811
0.4%
10.250509261
0.4%
10.032391551
0.4%
101
0.4%

2019
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct14
Distinct (%)100.0%
Missing252
Missing (%)94.7%
Infinite0
Infinite (%)0.0%
Mean7.213948458
Minimum0.4399999976
Maximum17.86131287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2022-04-03T11:39:30.960617image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.4399999976
5-th percentile1.144246617
Q13.701287508
median7.534258127
Q39.075346231
95-th percentile16.22669096
Maximum17.86131287
Range17.42131287
Interquartile range (IQR)5.374058723

Descriptive statistics

Standard deviation5.067421831
Coefficient of variation (CV)0.7024477455
Kurtosis0.1761909869
Mean7.213948458
Median Absolute Deviation (MAD)3.103185415
Skewness0.724659708
Sum100.9952784
Variance25.67876401
MonotonicityNot monotonic
2022-04-03T11:39:31.054340image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0.43999999761
 
0.4%
9.3000001911
 
0.4%
4.5569953921
 
0.4%
3.51
 
0.4%
7.8685164451
 
0.4%
15.346509931
 
0.4%
10.51
 
0.4%
17.861312871
 
0.4%
1.5234563351
 
0.4%
8.4013843541
 
0.4%
Other values (4)4
 
1.5%
(Missing)252
94.7%
ValueCountFrequency (%)
0.43999999761
0.4%
1.5234563351
0.4%
1.8728574511
0.4%
3.51
0.4%
4.3051500321
0.4%
4.5569953921
0.4%
7.1999998091
0.4%
7.8685164451
0.4%
8.3190956121
0.4%
8.4013843541
0.4%
ValueCountFrequency (%)
17.861312871
0.4%
15.346509931
0.4%
10.51
0.4%
9.3000001911
0.4%
8.4013843541
0.4%
8.3190956121
0.4%
7.8685164451
0.4%
7.1999998091
0.4%
4.5569953921
0.4%
4.3051500321
0.4%

2020
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct3
Distinct (%)100.0%
Missing263
Missing (%)98.9%
Memory size10.6 KiB
13.5
7.69999980926514
8.46224021911621

Length

Max length16
Median length16
Mean length12
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st row13.5
2nd row7.69999980926514
3rd row8.46224021911621

Common Values

ValueCountFrequency (%)
13.51
 
0.4%
7.699999809265141
 
0.4%
8.462240219116211
 
0.4%
(Missing)263
98.9%

Length

2022-04-03T11:39:31.166070image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-03T11:39:31.235879image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
13.51
33.3%
7.699999809265141
33.3%
8.462240219116211
33.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-04-03T11:39:18.276890image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:21.448424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:24.014308image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:26.500201image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:28.720888image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:31.247018image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:33.573424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:36.147960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:38.414785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:40.830623image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:42.993494image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:45.310664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:47.823026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:50.077266image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:52.618909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:54.893555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:57.279509image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:59.487087image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:01.958052image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:04.049196image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:06.471421image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:08.776774image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:11.262942image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:13.552497image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:16.018917image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:18.356674image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:21.804043image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:24.096117image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:26.589933image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:28.800701image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:31.360713image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:33.674874image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:36.238697image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:38.500559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:40.910381image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:43.079266image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:45.678018image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:47.905800image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:50.157053image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:52.699234image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:54.971346image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:57.377244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:59.590814image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:02.042825image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:04.133968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:06.556197image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:08.865536image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:11.348686image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:13.644280image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:16.110730image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:18.437430image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:21.885820image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:24.168996image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:26.679693image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:28.891431image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:31.452468image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:33.790535image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:36.370340image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:38.593307image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:40.995214image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:43.172021image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:45.767813image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:47.993566image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:50.523046image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:52.779062image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:55.325972image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:57.459996image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:59.666645image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:02.130589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:04.212763image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:06.643517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:08.944886image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:11.435453image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:13.728050image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:16.192512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:18.523200image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:21.984555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:24.260779image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:26.776710image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:28.980222image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:31.544223image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:33.881292image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:36.460121image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:38.679259image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:41.074572image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:43.262748image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:45.844576image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:48.082332image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:50.621810image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:52.881806image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:55.412736image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:57.547292image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:59.759395image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:02.209378image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:04.306513image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:06.728296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:09.038209image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:11.535186image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:13.817815image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:16.279305image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:18.609996image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:22.076850image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:24.348544image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:26.864014image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:29.077847image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:31.626031image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:33.969058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:36.543901image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:38.775025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:41.152839image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:43.355501image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:45.924390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:48.174800image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:50.713536image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:52.979517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:55.499504image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:57.632070image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:59.847165image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:02.293127image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:04.383307image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:06.816032image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:09.122013image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:11.623972image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:13.911563image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:16.364650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:18.694769image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:22.188034image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:24.440291image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:26.953282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:29.157701image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:31.706818image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:34.058817image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:36.635628image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:38.865360image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:41.247612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:43.451271image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:46.027820image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:48.267554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:50.807313image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:53.077283image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:55.585247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:57.724014image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:00.213401image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:02.371917image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:04.472041image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:06.911804image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:09.217788image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:11.716728image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:13.999866image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:16.451952image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:18.777548image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:22.287846image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:24.542994image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:27.038056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:29.239481image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:31.788624image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:34.161570image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:36.726410image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:38.949164image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:41.343357image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:43.545020image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:46.126584image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:48.365290image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:50.902990image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:53.168041image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:55.671018image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:57.813840image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:00.300167image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:02.449735image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:04.552691image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:06.998571image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:09.312535image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:11.803182image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:14.093097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:16.541735image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:18.863293image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-04-03T11:38:23.833821image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:26.331119image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:28.548341image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:31.065531image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:33.389105image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:35.969903image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:38.230279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:40.383979image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:42.828934image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:45.131143image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:47.628208image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:49.898744image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:52.437828image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:54.722019image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:57.095999image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:59.258029image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:01.773522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:03.862706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:06.284873image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:08.590300image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:11.076509image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:13.359042image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:15.827564image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:18.088391image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:20.649268image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:23.928566image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:26.414404image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:28.635578image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:31.158256image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:33.485631image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:36.061193image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:38.330012image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:40.476294image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:42.913711image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:45.215916image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:47.731245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:49.982520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:52.533605image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:54.807792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:57.182765image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:38:59.394149image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:01.867756image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:03.955451image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:06.376643image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:08.682029image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:11.167690image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:13.458775image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:15.925167image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T11:39:18.184135image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-04-03T11:39:31.413408image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-03T11:39:32.249356image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-03T11:39:33.076116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-03T11:39:33.840679image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-03T11:39:21.091061image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-04-03T11:39:21.980722image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-04-03T11:39:22.833517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

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Last rows

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